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Menampilkan 1–4 dari 4 artikel
Analisis Segmentasi Pelanggan Berbasis RFM dan Evaluasi Efektivitas Kampanye Pemasaran untuk Meningkatkan Retensi
Andy Hermawan
; Fachmi Aditama
; Lintang Rizki Ramadhani
; Nuur Muhammad Ilham
; Aji Saputra
; Nila Rusiardi Jayanti
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Vol 2
, No 4
(2024)
This research implements RFM (Recency, Frequency, Monetary) analysis to perform customer segmentation and evaluate the effectiveness of marketing campaigns in a retail company. Using a Kaggle dataset, this study identifies customers based on purchasing behaviour and assesses marketing campaign responses for each segment. The analysis reveals that Loyal, VIP, and New Customer segments showed the highest responses, especially in Campaign 6. The findings emphasize the importance of targeting resour...
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Leveraging the RFM Model for Customer Segmentation in a Software-as-a-Service (SaaS) Business Using Python
Andy Hermawan
; Nila Rusiardi Jayanti
; Aji Saputra
; Army Putera Parta
; Muhammad Abizar Algiffary Thahir
; Taufiqurrahman Taufiqurrahman
Maeswara : Jurnal Riset Ilmu Manajemen dan Kewirausahaan
Vol 2
, No 5
(2024)
Customer segmentation plays a pivotal role in driving marketing strategies and improving customer retention across various industries. This study explores the application of the RFM (Recency, Frequency, Monetary) model for customer segmentation in a Software-as-a-Service (SaaS) business, using Python for efficient data processing and analysis. By analyzing one year of customer purchase data, we segmented customers into key groups such as "Champions," "Loyal Customers," and "At Risk." The results...
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Optimalisasi Strategi Pemasaran Melalui Analisis RFM pada Dataset Transaksi Ritel Menggunakan Python
Andy Hermawan
; Nila Rusiardi Jayanti
; Aji Saputra
; Cahaya Tambunan
; Dzaky Muhammad Baihaqi
; Muhammad Alif Syahreza
; Zacharia Bachtiar
Jurnal Manajemen Riset Inovasi
Vol 2
, No 4
(2024)
This study aims to optimize marketing strategies through RFM (Recency, Frequency, Monetary) analysis on a retail transaction dataset obtained from Kaggle. The dataset contains 64,682 transactions from 5,242 SKUs involving 22,625 customers over one year. Data cleaning and RFM analysis were conducted to segment customers based on recency, frequency, and monetary values. The findings reveal that customers were segmented into groups such as Champions, Loyal Customers, and At Risk. These segments pro...
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Membangun Model Prediksi Churn Pelanggan yang Akurat: Studi Kasus tentang TELCO Company
Andy Hermawan
; Nila Rusiardi Jayanti
; Zia Tabaruk
; Faizal Lutfi Yoga Triadi
; Aji Saputra
; M.Rahmat Hidayat Syachrudin
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Vol 2
, No 6
(2024)
Customer churn prediction models have become an important tool in the telecommunications industry to reduce churn rates and improve customer retention. This research focuses on building an accurate customer churn prediction model using machine learning algorithms for TELCO Company. By applying diverse feature engineering techniques and prediction models such as RandomForestClassifier, DecisionTreeClassifier, and XGBoost, this study showcases a significant improvement in prediction accuracy compa...
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